Commentary: Shared, automated… and electric?



28 March 2019

What are the implications of emerging mobility technologies and trends on the future of electric vehicles? (Photograph: Shutterstock)

Automated driving and shared mobility could dramatically reshape road transport over the coming decades, with major implications for vehicle electrification and the broader electricity system. But can we assume that shared and/or autonomous vehicles of the future will be electric?

While electric vehicles (EVs) tend to be more expensive to purchase, they have lower fuel and maintenance costs than conventional vehicles. As shared and/or autonomous fleets would typically have heavier use patterns than with privately owned vehicles, the lower running costs could make EVs cheaper overall. But whether EVs could fulfil all the operational and technical requirements of shared and/or autonomous vehicles is less certain.

Building upon our look at emerging mobility technologies and services, we discuss the opportunities and challenges of electrifying shared mobility car fleets today and examine prospects for electrifying autonomous vehicles in the future. We explore how we might need to begin to re-think EV-related policies and investments to capitalise on synergies between the three revolutions – sharing, automation and electrification.

Shared and electric?

Car sharing services, which emerged in major cities in the early 2000s, allow members to borrow cars on a short-term basis. As car sharing fleets tend to have shorter trip distance profiles and higher utilisation rates compared to privately owned vehicles, EVs might be a good fit. In fact, several car sharing programs already operate all-electric fleets, including Moov’in.Paris, BlueSG (Singapore), Carma (San Francisco), car2go (Stuttgart, Amsterdam, Madrid, Paris), and DriveNow (Copenhagen).

Most car sharing services operate in one of two ways: free-floating systems where cars can be parked anywhere, or hub/depot services where cars must be left in designated parking spots. In recent years, smartphones and mobile connectivity have made free-floating systems (and by extension one-way journeys) easier to access and pay for.

But free-floating systems using EVs face operational challenges as they rely on a limited number of public fast chargers. These challenges could be overcome through larger batteries, a better-designed charging network (e.g. faster chargers, more stations), or user incentives. In comparison, hub/depot car sharing systems can schedule slower and cheaper charging on their own chargers during vehicle downtimes.

Just as smartphones have changed the way car sharing services operate, they have fostered the rapid expansion of app-based ride-sourcing services provided by so-called transportation network companies (TNCs) such as Uber, Lyft, Didi Chuxing and GrabTaxi. The adoption of EVs in TNC fleets has been slow, despite the significant fuel and maintenance savings potential of EVs for full-time drivers working with TNCs. EV shares on the major ride-sourcing platforms remain below 1% with the exception of Didi at 1.3%, which already has over 400 000 EVs on its network. In California, EVs represented about 1% of vehicle share and trip miles in 2017.

There are also several barriers to EV adoption in taxis and ride-sourcing fleets. First, EVs are generally more expensive to purchase, and few EV models available today meet all the operational requirements of taxis and ride-sourcing services – notably long electric range, seat capacity and large trunk space.

Second, the combination of limited driving range, long charge times, and/or limited access to fast charging can pose challenges – searching for available chargers and long charging times could mean foregone revenues for drivers. Some taxi fleets are demonstrating the use of fuel cell electric vehicles (FCEVs) which could address some of these operational challenges.

Third, TNCs have limited ability to influence purchase decisions of their drivers, including in most jurisdictions where they cannot specify the use of particular vehicle models. But several TNCs are initiating programs to encourage usage of EVs on their platforms. Uber’s Clean Air Program in London provides financial incentives to drivers to switch to or drive more in EVs while Lyft ExpressDrive’s short-term lease options allow drivers to try EVs with little risk. Maven, GM’s car-sharing spin-off, offers a service of short-term rentals of the Chevrolet Bolt BEV to drivers working for TNCs and other shared platforms.

Shifting to EVs for car sharing and TNCs could lead to much larger per-vehicle reductions in GHG and local pollutant emissions compared to privately owned EVs. High utilisation and faster fleet turnover could also help to accelerate battery innovation cycles and more rapid adoption of increasingly efficient vehicles. In addition, given the importance of EV awareness and experience in influencing purchase decisions, the potential exposure of the benefits of electric drive to millions of potential car buyers could indirectly help to increase adoption of privately owned EVs.

Autonomous and electric?

Meanwhile, rapid advances in sensing technologies, connectivity, and AI are bringing highly automated vehicles – autonomous vehicles (AVs) – closer to market. Waymo recently launched their self-driving car service, Waymo One, while major automakers have announced plans to introduce AVs as early as 2020.

Just as with shared mobility and electrification, there are synergies between automation and electrification. With high utilisation rates, commercial fleet applications (where early adoption of AVs seems likely) tend to favour powertrains with lower operations and maintenance costs, including EVs. Well-coordinated fleets of electric AVs may be able to manage challenges around range, access to charging infrastructure, and charging time management. Automated driving technologies may also be easier to implement in EVs due to the greater number of drive-by-wire components.

	Maintenance and Wear	Interest	Depreciation	Insurance	Tax	Parking and Tolls	Fuel	Cleaning	Salaries	Overhead and Vehicle Operations 	Public Transport (sum)
Private CV	0.056	0.033	0.203	0.044	0.011	0.068	0.056	0.005			
Private EV	0.097	0.033	0.203	0.029	0	0.068	0.028	0.005			
Private A-CV	0.055	0.04	0.243	0.022	0.011	0.068	0.051	0.005			
Private A-EV	0.096	0.04	0.243	0.014	0	0.068	0.025	0.005			
Taxi CV	0.045	0.002	0.061	0.007	0.002	0.031	0.057	0.026	2.409	0.083	
Taxi EV	0.078	0.002	0.061	0.005	0	0.031	0.028	0.026	2.409	0.083	
Taxi A-CV	0.044	0.002	0.073	0.003	0.002	0.03	0.051	0.117		0.08	
Taxi A-EV	0.076	0.002	0.073	0.002	0	0.03	0.026	0.117		0.08	
Taxi A-EV Pool	0.045	0.001	0.043	0.001	0	0.018	0.015	0.117		0.047	
Bus EV											0.501658
Bus A-EV											0.2257461
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Source: Bösch, P. M., Becker, F., Becker, H., & Axhausen, K. W. (2018). Cost-based analysis of autonomous mobility services. Transport Policy, 64, 76–91. https://doi.org/10.1016/j.tranpol.2017.09.005.

However, higher utilisation rates of commercial AVs will also mean greater travel distances per day, requiring larger and more expensive battery packs or more frequent recharging (and downtime). AVs may also require significant power consumption to power on-board electronics, though the efficiency of these chips is improving rapidly, from 3‑5 kW in the first generation to less than 1 kW today.

While there is considerable debate regarding how quickly (and if ever) AVs will enter the mainstream, there are specific use cases where the feasibility and economics favour early adoption. For example, commercial applications where labour costs are high or where automation could enable higher vehicle utilisation (e.g. trucks, buses, taxis and ride-sourcing) have the largest potential for cost-cutting through automation.

Pilots and trials are underway for these applications in over 80 cities around the world, and nearly all are using some form of electrified vehicle. Notable examples include robotaxis from Waymo and nuTonomy/Lyft, autonomous electric shuttles across cities in Europe and North America, and autonomous electric buses in Asia. In California, EVs now account for around 70% of automated vehicle trial miles (mostly plug-in hybrids).

A growing number of trials of autonomous electric urban delivery vehicles are also being undertaken in a number of cities in China and the United States. While testing of autonomous freight trucks has been limited to date, early models and concepts from Einride, Ford, and Volvo  suggest a push towards all-electric. Tesla’s all-electric Semi is equipped with Enhanced Autopilot (equating to SAE Level 2 automation), which allows for automatic lane-keeping, forward collision warning, and automatic emergency braking.

Shared, autonomous and electric vehicles… and the grid

Governments, utilities, and other companies are actively working to build out charging infrastructure to support the growing number of EVs. Recent research (here, here, and here) shows how public charging infrastructure in particular will be critical in catalysing further market uptake of personally owned electric cars.

For fleets, their intensive and distinct use patterns imply greater (and different) needs for charging compared to private EVs. The availability and coverage of public and fast chargers could be a critical factor in how quickly these fleets become electric, and how business models evolve around shared and/or automated mobility.

Distribution of public EV chargers in Vancouver (left) and Amsterdam (right)

Source: PlugShare

EVs currently make up only about 1% of all passenger cars globally, but clustering effects in EV adoption at the local level, combined with uncoordinated charging, could cause problems for the distribution grid, and eventually require greater investments in power generation and transmission.

A combination of pricing incentives and digital technologies (including, eventually, coordinated discharging of EV batteries) could better coordinate fleet and private charging of EVs, minimising negative grid impacts, reducing CO2 emissions, and providing ancillary services. A transition to shared, automated, and electric vehicle (SAEV) fleets could also yield significant system-wide benefits for the grid, assuming the necessary digital technologies and incentive structures are in place.

Researchers are already looking at how different fleet compositions of SAEVs and charger availability could impact costs, operations, and grid impacts. For instance, fleet simulations in Austin, Texas (2016, 2018); Zurich, Switzerland (2016); Columbus, Ohio (2018); and Tokyo, Japan (2019) have investigated how varying fleet size, electric range, charger speed, and pooling could impact vehicle travel patterns and wait times. As the electric fleets modelled in these simulations begin to roll out in the real world, empirical data will lead to a far more robust and deep understanding of the opportunities and trade-offs of SAEVs.

In the near-term, appropriate data sharing between policy makers, utilities, and fleet operators could help anticipate needs for charging infrastructure as mobility service fleets electrify. Over the long-term, shifts towards SAEV fleets could improve the economics of charging infrastructure by increasing utilisation, promoting faster returns on investments and reducing reliance on subsidies and indirect revenue streams through grid services. Utilities could also explore rate structures that maximise grid benefits. Volumetric energy rates based on hourly wholesale pricing, for instance, may be a promising means of reducing peak loading and promoting charging at times when variable renewables are at their peak.

Policies and strategies to electrify a shared and/or automated future

National, regional, and municipal governments around the world are implementing a range of policies to encourage EV adoption and use. Country (and city)-specific objectives, constraints, and contexts will continue to shape the design of appropriate policy mixes for each jurisdiction.

Purchase incentives have generally been effective in encouraging the purchase of EVs, in turn helping to stimulate investment and bring down costs of battery and EV production. Mandates that car manufacturers produce minimum volumes of EVs (i.e. ZEV mandates) have complemented these by providing supply-side certainty.

But with growing adoption of shared (and potentially autonomous) mobility, the importance of policies designed to more directly incentivise the use of EVs over conventional vehicle travel will grow. These policies could include fuel taxes, zero-emission zones, road pricing, HOV and transit lane access, incentives for electric mobility services, or even restrictions on the use of conventional vehicles. Supporting the build-out of charging infrastructure will continue to be crucial to further EV adoption and use, including fast-charging infrastructure in densely populated metropolises and a robust charging network to support a transition to all-electric fleets. Cities where taxi and bus fleets are already making the transition to electric drive may be able to leverage fast-charging stations built for these fleets to spur a transition to electric shared mobility.

Researchers and policymakers are exploring alternative policy frameworks that could be effective in promoting electrification of shared and, eventually, autonomous fleets. California’s SB-1014 “California Clean Miles Standard and Incentive Program: zero-emission vehicles” approved in September 2018 aims to establish annual emission reduction targets for TNCs per passenger-mile. London’s Ultra Low Emissions Zone encourages for all road users, including fleets, to switch to EVs.

Given the uncertainty in how emerging trends could reshape mobility, policymakers might look to more flexible and forward-looking policies and strategies to get ready for different futures.

There may already be useful lessons learned on EV policy and infrastructure planning from cities with high rates of electrified taxis and buses such as Shenzhen, Amsterdam and Santiago. Electric bus depots or other centralised charging hubs could also serve mobility service fleets of the future, supplementing or even servicing the majority of charging needs. Such hubs could be located outside of cities, where property values (not to mention constraints on high voltage installations) are lower. But there may be systems-level repercussions to relying on such a strategy: it could lead to more traffic congestion and lower operational service efficiency from increased “deadheading”.

Dynamics are likely to differ between cities and geographies, driven by differences in power generation mixes and in mobility patterns. Simulations and case studies can begin to illustrate the levers behind such differences, and to anticipate the potential transformations that might occur if, and when, cars and buses become fully autonomous.

To help inform the design of flexible and forward-looking policies, research needs to continue to improve our understanding of a few key questions:

  • How do the charging needs of fleets differ from those of privately owned cars and in different geographic contexts? How can public charging infrastructure work to support the electrification of fleets and promote driving on electricity?
  • How might automated fleets change investment decisions around charging infrastructure, including the economics of wireless charging or battery swapping? What business models, data sharing, or policy is needed to balance charging infrastructure needs to support mobility service fleet operations and grid operations?
  • What are the energy and emissions implications of various market and regulatory designs of power markets? How can they facilitate the transition to renewable and low-carbon energy generation?

Electrifying vehicles can reduce some of the environmental impacts of mobility, notably local air pollution and greenhouse gas emissions. But other adverse effects on society could be exacerbated by emerging mobility technologies and trends, including congestion, inequality, and mobility access issues. Policy makers will need to implement comprehensive policy packages that guard against these challenges. We will explore these and other critical issues in upcoming commentaries.


Learn more

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